Identify the decisions
Train leaders to recognize practical uses of AI and prioritize the decisions that matter to the organization and its members.
Data & AI for utilities and energy co-ops
The decisionis hard enough.Trust the inputs.
An unexpected load shift. The same equipment issue again. A forecast you have to explain. The evidence can sit across years of history and disconnected systems.
We help leaders identify where data and AI can improve forecasting, operations, and member service. Then we build the data foundation and provide the expertise to develop and test those applications.
From operating history to useful evidence
- Operating history
- Market & weather data
- Engineering knowledge
A defensible
decision starts here.
Connected sources → Data quality and context → Analytics, AI, and workflow tools → Human judgment
Conceptual data-to-decision flow. No operating data shown.
An energy engagementA member-owned generation & transmission cooperative. A focused program with the Power Marketing team.
6month program scope
15candidate use cases ranked
3funded initiatives
Training. Priorities. Implementation.
Connect leadership priorities to the data.
Prepare the foundation
Connect relevant operating history and systems. Address quality, context, ownership, and access.
Develop and evaluate
Build the analytics, predictive tools, and workflows the use case requires. Test with the people responsible for the decisions.
Illustrative testimonial
We needed to connect the data before we could trust the forecast. The work had a clear sequence and an owner.
The right data lake. The right connections.
Make your history
usable.
A data lake gives data from different systems a shared home. Its value comes from reliable records, clear context, and appropriate access.
Start with the decision you need to improve. Identify its sources, the gaps that matter, and how current the information needs to be.
That may call for a governed data lake or lakehouse, or better connections to systems you already use. Assess the architecture against your workload, access requirements, and budget.
- 01
Connect the relevant history.
Approved telemetry, forecasts, asset records, work orders, market feeds, and engineering documents. Connect what the decision needs.
- 02
Make the records agree.
Reconcile asset IDs, timestamps, units, and definitions. Flag missing or suspect readings before they shape an analysis.
- 03
Keep the context attached.
Make source history, versions, and freshness visible. Give each important dataset an owner who understands what it means.
- 04
Draw the access boundaries.
Set permissions and approved data paths. Design how information reaches analysts and agents around your IT and OT boundaries.
Build for the first useful decision.
Extend when the next one earns its place.
Three illustrative workflows
A different decision.
A different toolset.
A forecast needs different tools from an evidence search. Connect the relevant data to the analysis, models, and workflows that fit the problem.
Which forecasts hold up when conditions change?
A way to examine the forecasts behind trading and operating decisions.
01 / Relevant data
Forecasts and what happened
Historical forecast versions, actual load and prices, weather, and operating conditions.
02 / Data foundation
A fair basis for comparison
Align timestamps and units. Preserve what was known when each forecast was issued.
03 / Tools for the job
Forecast evaluation
Compare error and bias by model, time horizon, and conditions. Track changes in performance.
Useful result
A comparison showing where each forecast performs well, where it struggles, and which data gaps limit confidence.
Your team's decision
Decide which models deserve further testing. Run candidates beside the current process before relying on them.
What could explain the same equipment issue returning?
A way to bring operational signals and maintenance context into the same investigation.
01 / Relevant data
Signals and service history
Historian readings, alarm records, work orders, equipment details, and engineering manuals.
02 / Data foundation
The same asset, in context
Match asset IDs and time periods. Check sensor quality, missing readings, and maintenance history.
03 / Tools for the job
Pattern analysis and search
Examine unusual trends alongside past repairs. Retrieve relevant documentation with source links.
Useful result
An investigation brief with observed changes, related maintenance events, supporting records, and open questions.
Your team's decision
Engineers decide what needs inspection. Findings support their investigation and established operating procedures.
How much effort goes into assembling the same evidence?
A way to prepare recurring reporting and audit materials with their sources attached.
01 / Relevant data
Records and requirements
Approved procedures, operating records, prior submissions, and the requirements your team identifies.
02 / Data foundation
Evidence you can trace
Preserve versions, source locations, and ownership. Apply permissions to the material each reviewer can access.
03 / Tools for the job
Search, drafting, and tracking
Find relevant records, assemble a draft evidence pack, and identify missing items and review owners.
Useful result
A draft package with source links, unresolved questions, and a clear list of material still needed.
Your team's decision
Responsible staff validate the evidence. They approve what is complete, accurate, and ready to submit.
Illustrative workflows. Data connections, tools, review steps, and expected results are scoped and tested for each engagement.
Progress your operators can trust
It has to earn its
place in operations.
Your team has to explain the decision and live with the result. Concerns about unreliable data or an open-ended platform project deserve to shape the plan.
01
Define the decision.
Choose a costly, recurring problem. Agree on the evidence, the decision owner, and the improvement that would justify the work.
02
Connect and equip.
Assess the foundation. Build the necessary connections and quality checks. Select analytics, AI, or workflow tools to fit the task.
03Test, measure,
Test, measure,
and earn trust.
Evaluate historical results and test beside the current process where appropriate. Involve the people who will use, review, and maintain the solution.
If the data cannot support the decision, or the value does not justify the work, we'll recommend fixing that gap or stopping.
Start with the question that matters
What is the hardest
decision your data
should help you make?
Bring the problem and a picture of the systems behind it. Work through the data, tools, and first useful step with BiG Impact Group.
Talk through the hard problemA 30-minute discovery call.
A page for the next internal conversation
Plan the work with your leadership team.
We help leaders identify where data and AI can improve forecasting, operations, and member service. Then we build the data foundation and provide the expertise to develop and test those applications.
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